BugPredict

Defect Prediction in Software Repositories: A Comprehensive Analysis

1. Introduction

In this analysis after cleaning up the BugTool, I explored defect prediction using a historical commit-based approach (Approach A) on two types of repositories: a large, highly active repository (OpenJ9) and a small, less active repository (Hiero). By leveraging historical commit data, I assessed the effectiveness of this method in identifying defect-prone files and evaluated its scalability, accuracy, and limitations.

2. Objectives

The primary objectives of this study were:

• To evaluate the effectiveness of historical commit-based defect prediction.
• To compare the behavior of defect scores between large and small repositories.
• To visualize the correlation between commit frequency and defect likelihood.
• To identify strengths, weaknesses, and practical implications of this approach.

3. Methodology

To conduct this analysis, I followed these steps:

3.1 Data Collection

I obtained repository data from two sources:

• Large Repository (OpenJ9) – A highly active repository with thousands of commits. The openj9 metrics data can be found here

  • Am able to collect the above metrics and store them in an online mongodb cluster as shown in the screenshot below
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• Small Repository (Hiero) – A repository with significantly fewer commits and contributors. The hiero metrics data can be found here

  • Am able to collect the above metrics and store them in an online mongodb cluster as shown in the screenshot below
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Each repository's data included:

• File Name: The source code file being analyzed.
• Defect Score: A numerical measure indicating the likelihood of defects based on past commit history.
• Commit History: A list of commit messages and timestamps for each file.

3.2 Tools Used

To process and analyze the data, I used:

• Python (Pandas, Matplotlib, Seaborn): For data manipulation and visualization.
• Jupyter Notebook: To facilitate interactive analysis.
• JSON Processing: To parse and extract relevant repository data.

3.3 Data Processing

I structured the data as follows:

• Extracted defect scores and commit counts from the JSON files.
• Created a structured DataFrame using Pandas.
• Filtered out irrelevant or low-activity files.
• Plotted scatter graphs to visualize commit frequency vs. defect score.

4. Data Processing and Preprocessing

4.1 Large Repository (OpenJ9)

The large repository dataset contained thousands of commits per file. The defect scores ranged from 10.4 (highest risk) to near zero (lowest risk). The highest-ranked file was runtime/oti/j9nonbuilder.h, which had a defect score of 10.3999 with numerous commit changes, indicating strong correlation between high commit frequency and defect likelihood.

4.2 Small Repository (Hiero)

The small repository dataset showed significantly lower defect scores, with the highest being 0.3253 for TestConfigSource.java. Unlike the large repository, even files with multiple commits had relatively low defect scores, suggesting that the historical commit-based approach may be less effective in smaller repositories due to limited commit data.

5. Analysis & Findings

5.1 Correlation Between Commit Frequency and Defect Score

• Large Repository: High commit frequency correlated with higher defect scores, validating the assumption that frequently modified files are more defect-prone.
• Small Repository: No clear correlation; defect scores remained low across files regardless of commit frequency.

5.2 Scatter Plot Visualization

I generated scatter plots to visualize the relationship between commit count and defect score:

• Blue dots (Large Repository): Showed a clear upward trend, indicating a positive correlation.
• Red dots (Small Repository): Were scattered with no clear trend, reinforcing the idea that this method is less effective for small repositories.

6. Strengths and Weaknesses of Approach A

6.1 Strengths

• Scalability: Works well for large repositories with extensive commit history.
• Low Computational Cost: Does not require deep code analysis, only commit history.
• Quick Insights: Identifies high-risk files efficiently.

6.2 Weaknesses

• Limited Effectiveness in Small Repositories: Insufficient commit history results in unreliable defect scores.
• Cannot Detect New Bugs: Only identifies defects based on past fixes.
• Repository-Dependent Behavior: Relies on repositories having a history of frequent bug fixes.

7. Challenges Encountered

7.1 Data Cleaning Issues

• Extracting structured information from JSON required careful preprocessing.
• Some files had inconsistent commit history, requiring additional filtering.

7.2 Interpretation Challenges

• Small repositories posed difficulties in deriving meaningful trends.
• Need for additional methods (e.g., static analysis) to complement historical defect prediction.

8. Technical Implications

The findings suggest that historical commit-based defect prediction is highly effective for large repositories but struggles with smaller ones. Organizations relying on this approach should:

• Complement it with static code analysis for small repositories.
• Focus on highly active files in large repositories for defect mitigation.
• Use a hybrid model that integrates multiple defect prediction techniques.

9. Conclusion & Recommendations

9.1 Key Takeaways

• Large repositories benefit greatly from this method.
• Small repositories need alternative approaches for better defect predictions.
• Commit frequency is a strong indicator of defect-prone files in active repositories.

9.2 Next Steps

To enhance the accuracy of defect prediction, I recommend:

• Combining historical commit-based analysis with machine learning models.
• Applying static analysis techniques for small repositories.
• Exploring deep learning methods for better defect classification.